Multiplierless Spiking Neural Network for Motor Signal Decoding in the Peripheral Nervous System

The peripheral nervous system (PNS) facilitates communication between the brain and various organs. Advanced PNS neural interfaces can help in restoring motor functions for patients suffering from spinal cord injuries, amputations, and other conditions. The efficacy of these neural interfaces, and the precision of sensory activity decoding is heavily reliant on artificial intelligence techniques at the backend. Traditional machine learning techniques, such as feature-based classification, generally requires extensive feature engineering and domain expertise, and is often ineffective at handling very high-dimensional data. Additionally, convolutional neural networks (CNNs) tend to perform best when employing floating-point operations, which inturn can be computationally intensive and less energy-efficient. Addressing these challenges, this paper presents a multiplier-less spiking neural network (SNN) that utilizes fewer neurons to achieve higher accuracy. Leveraging the discrete firing characteristics of SNNs, we propose a hardware implementation model, trained on experimentally recorded action potentials from rat peripheral nerves. This demonstrates a significant improvement in Macro F1-score, reaching 0.89, over various CNNs while using $\mathbf{9 9. 8 \%}$ fewer parameters. It fully decodes three degrees of rat motion (dorsiflexion, plantarflexion, and pricking stimulation), showcasing the potential for efficient and accurate hardware integration. This work highlights a path towards the development of next-generation hardware-assisted neural interfaces.